001. Part 1
002. Part 1
003. Chapter 1. Welcome to the Kafka event streaming platform
004. Chapter 1. What is an event
005. Chapter 1. An event stream example
006. Chapter 1. Introducing the Apache Kafka event streaming platform
007. Chapter 1. A concrete example of applying the Kafka event streaming platform
008. Chapter 1. Summary
009. Chapter 2. Kafka brokers
010. Chapter 2. Produce requests
011. Chapter 2. Fetch requests
012. Chapter 2. Topics and partitions
013. Chapter 2. Sending your first messages
014. Chapter 2. Segments
015. Chapter 2. Tiered storage
016. Chapter 2. Cluster metadata
017. Chapter 2. Leaders and followers
018. Chapter 2. Checking for a healthy broker
019. Chapter 2. Summary
020. Part 2
021. Chapter 3. Schema Registry
022. Chapter 3. What is a schema, and why do you need one
023. Chapter 3. Subject name strategies
024. Chapter 3. Schema compatibility
025. Chapter 3. Schema references
026. Chapter 3. Schema references and multiple events per topic
027. Chapter 3. Schema Registry (de)serializers
028. Chapter 3. Serialization without Schema Registry
029. Chapter 3. Summary
030. Chapter 4. Kafka clients
031. Chapter 4. Producing records with the KafkaProducer
032. Chapter 4. Consuming records with the KafkaConsumer
033. Chapter 4. Exactly-once delivery in Kafka
034. Chapter 4. Using the Admin API for programmatic topic management
035. Chapter 4. Handling multiple event types in a single topic
036. Chapter 4. Summary
037. Chapter 5. Kafka Connect
038. Chapter 5. Integrating external applications into Kafka
039. Chapter 5. Getting started with Kafka Connect
040. Chapter 5. Applying Single Message Transforms
041. Chapter 5. Adding a sink connector
042. Chapter 5. Building and deploying your own connector
043. Chapter 5. Summary
044. Part 3
045. Chapter 6. Developing Kafka Streams
046. Chapter 6. Kafka Streams DSL
047. Chapter 6. Hello World for Kafka Streams
048. Chapter 6. Masking credit card numbers and tracking purchase rewards in a retail sales setting
049. Chapter 6. Interactive development
050. Chapter 6. Choosing which events to process
051. Chapter 6. Summary
052. Chapter 7. Streams and state
053. Chapter 7. Adding stateful operations to Kafka Streams
054. Chapter 7. Stream-stream joins
055. Chapter 7. State stores in Kafka Streams
056. Chapter 7. Summary
057. Chapter 8. The KTable API
058. Chapter 8. KTables are stateful
059. Chapter 8. The KTable API
060. Chapter 8. KTable aggregations
061. Chapter 8. GlobalKTable
062. Chapter 8. Table joins
063. Chapter 8. Summary
064. Chapter 9. Windowing and timestamps
065. Chapter 9. Handling out order data with grace literally
066. Chapter 9. Final windowed results
067. Chapter 9. Timestamps in Kafka Streams
068. Chapter 9. The TimestampExtractor
069. Chapter 9. Stream time
070. Chapter 9. Summary
071. Chapter 10. The Processor API
072. Chapter 10. Digging deeper into the Processor API with a stock analysis processor
073. Chapter 10. Data-driven aggregation
074. Chapter 10. Integrating the Processor API and the Kafka Streams API
075. Chapter 10. Summary
076. Chapter 11. ksqlDB
077. Chapter 11. More about streaming queries
078. Chapter 11. Persistent vs. push vs. pull queries
079. Chapter 11. Creating Streams and Tables
080. Chapter 11. Schema Registry integration
081. Chapter 11. ksqlDB advanced features
082. Chapter 11. Summary
083. Chapter 12. Spring Kafka
084. Chapter 12. Using Spring to build Kafka-enabled applications
085. Chapter 12. Spring Kafka Streams
086. Chapter 12. Summary
087. Chapter 13. Kafka Streams Interactive Queries
088. Chapter 13. Learning about Interactive Queries
089. Chapter 13. Summary
090. Chapter 14. Testing
091. Chapter 14. Summary
092. appendix A. Schema compatibility workshop
093. appendix A. Forward compatibility
094. appendix A. Full compatibility
095. appendix B. Confluent resources
096. appendix B. Confluent command-line interface
097. appendix B. Confluent local
098. appendix C. Working with Avro, Protobuf, and JSON Schema
099. appendix C. Protocol Buffers
100. appendix C. JSON Schema
101. appendix D. Understanding Kafka Streams architecture
102. appendix D. Consumer and producer clients in Kafka Streams
103. appendix D. Assigning, distributing, and processing events
104. appendix D. Threads in Kafka Streams StreamThread
105. appendix D. Processing records
